Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimizing non-natural protein function with directed evolution.

ABSTRACT: Developing technologies such as unnatural amino acid mutagenesis, non-natural cofactor engineering, and computational design are generating proteins with novel functions; these proteins, however, often do not reach performance targets and would benefit from further optimization. Evolutionary methods can complement these approaches: recent work combining unnatural amino acid mutagenesis and phage selection has created useful proteins of novel composition. Weak initial activity in a computationally designed enzyme has been improved by iterative rounds of mutagenesis and screening. A marriage of ingenuity and evolution will expand the scope of protein function well beyond Mother Nature's designs.

SUBMITTER: Brustad EM

PROVIDER: S-EPMC3080047 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Optimizing non-natural protein function with directed evolution.

Brustad Eric M EM Arnold Frances H FH

Current opinion in chemical biology 20101223 2

Developing technologies such as unnatural amino acid mutagenesis, non-natural cofactor engineering, and computational design are generating proteins with novel functions; these proteins, however, often do not reach performance targets and would benefit from further optimization. Evolutionary methods can complement these approaches: recent work combining unnatural amino acid mutagenesis and phage selection has created useful proteins of novel composition. Weak initial activity in a computationall ...[more]

PMID: 21185770

Similar Datasets

Design of protein function leaps by directed domain interface evolution.

Project description:Most natural proteins performing sophisticated tasks contain multiple domains where an active site is located at the domain interface. Comparative structural analyses suggest that major leaps in protein function occur through gene recombination events that connect two or more protein domains to generate a new active site, frequently occurring at the newly created domain interface. However, such functional leaps by combination of unrelated domains have not been directly demonstrated. Here we show that highly specific and complex protein functions can be generated by joining a low-affinity peptide-binding domain with a functionally inert second domain and subsequently optimizing the domain interface. These directed evolution processes dramatically enhanced both affinity and specificity to a level unattainable with a single domain, corresponding to >500-fold and >2,000-fold increases of affinity and specificity, respectively. An x-ray crystal structure revealed that the resulting "affinity clamp" had clamshell architecture as designed, with large additional binding surface contributed by the second domain. The affinity clamps having a single-nanomolar dissociation constant outperformed a monoclonal antibody in immunochemical applications. This work establishes evolutionary paths from isolated domains with primitive function to multidomain proteins with sophisticated function and introduces a new protein-engineering concept that allows for the generation of highly functional affinity reagents to a predefined target. The prevalence and variety of natural interaction domains suggest that numerous new functions can be designed by using directed domain interface evolution.

| S-EPMC2373342 | biostudies-literature

Directed evolution of GFP with non-natural amino acids identifies residues for augmenting and photoswitching fluorescence.

Project description:Genetic code reprogramming allows proteins to sample new chemistry through the defined and targeted introduction of non-natural amino acids (nAAs). Many useful nAAs are derivatives of the natural aromatic amino acid tyrosine, with the para OH group replaced with useful but often bulkier substituents. Extending residue sampling by directed evolution identified positions in Green Fluorescent Protein tolerant to aromatic nAAs, including identification of novel sites that modulate fluorescence. Replacement of the buried L44 residue by photosensitive p-azidophenylalanine (azF) conferred environmentally sensitive photoswitching. In silico modelling of the L44azF dark state provided an insight into the mechanism of action through modulation of the hydrogen bonding network surrounding the chromophore. Targeted mutagenesis of T203 with aromatic nAAs to introduce ?-stacking with the chromophore successfully generated red shifted versions of GFP. Incorporation of azF at residue 203 conferred high photosensitivity on sfGFP with even ambient light mediating a functional switch. Thus, engineering proteins with non-natural aromatic amino acids by surveying a wide residue set can introduce new and beneficial properties into a protein through the sampling of non-intuitive mutations. Coupled with retrospective in silico modelling, this will facilitate both our understanding of the impact of nAAs on protein structure and function, and future design endeavours.

| S-EPMC5811120 | biostudies-literature

eCodonOpt: a systematic computational framework for optimizing codon usage in directed evolution experiments.

Project description:We present a systematic computational framework, eCodonOpt, for designing parental DNA sequences for directed evolution experiments through codon usage optimization. Given a set of homologous parental proteins to be recombined at the DNA level, the optimal DNA sequences encoding these proteins are sought for a given diversity objective. We find that the free energy of annealing between the recombining DNA sequences is a much better descriptor of the extent of crossover formation than sequence identity. Three different diversity targets are investigated for the DNA shuffling protocol to showcase the utility of the eCodonOpt framework: (i) maximizing the average number of crossovers per recombined sequence; (ii) minimizing bias in family DNA shuffling so that each of the parental sequence pair contributes a similar number of crossovers to the library; and (iii) maximizing the relative frequency of crossovers in specific structural regions. Each one of these design challenges is formulated as a constrained optimization problem that utilizes 0-1 binary variables as on/off switches to model the selection of different codon choices for each residue position. Computational results suggest that many-fold improvements in the crossover frequency, location and specificity are possible, providing valuable insights for the engineering of directed evolution protocols.

| S-EPMC117206 | biostudies-literature

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution.

Project description:Engineered small non-antibody protein scaffolds are a promising alternative to antibodies and are especially attractive for use in protein therapeutics and diagnostics. The advantages include smaller size and a more robust, single-domain structural framework with a defined binding surface amenable to mutation. This calls for a more systematic approach in designing new scaffolds suitable for use in one or more methods of directed evolution. We hereby describe a process based on an analysis of protein structures from the Protein Data Bank and their experimental examination. The candidate protein scaffolds were subjected to a thorough screening including computational evaluation of the mutability, and experimental determination of their expression yield in E. coli, solubility, and thermostability. In the next step, we examined several variants of the candidate scaffolds including their wild types and alanine mutants. We proved the applicability of this systematic procedure by selecting a monomeric single-domain human protein with a fold different from previously known scaffolds. The newly developed scaffold, called ProBi (Protein Binder), contains two independently mutable surface patches. We demonstrated its functionality by training it as a binder against human interleukin-10, a medically important cytokine. The procedure yielded scaffold-related variants with nanomolar affinity.

| S-EPMC7911045 | biostudies-literature

Exploring protein fitness landscapes by directed evolution.

Project description:Directed evolution circumvents our profound ignorance of how a protein's sequence encodes its function by using iterative rounds of random mutation and artificial selection to discover new and useful proteins. Proteins can be tuned to adapt to new functions or environments by simple adaptive walks involving small numbers of mutations. Directed evolution studies have shown how rapidly some proteins can evolve under strong selection pressures and, because the entire 'fossil record' of evolutionary intermediates is available for detailed study, they have provided new insight into the relationship between sequence and function. Directed evolution has also shown how mutations that are functionally neutral can set the stage for further adaptation.

| S-EPMC2997618 | biostudies-literature

Towards the directed evolution of protein materials.

Project description:Protein-based materials have emerged as a powerful instrument for a new generation of biological materials, with many chemical and mechanical capabilities. Through the manipulation of DNA, researchers can design proteins at the molecular level, engineering a vast array of structural building blocks. However, our capability to rationally design and predict the properties of such materials is limited by the vastness of possible sequence space. Directed evolution has emerged as a powerful tool to improve biological systems through mutation and selection, presenting another avenue to produce novel protein materials. In this prospective review, we discuss the application of directed evolution for protein materials, reviewing current examples and developments that could facilitate the evolution of protein for material applications.

| S-EPMC6867688 | biostudies-literature

Catalytically Enhanced Cas9 through Directed Protein Evolution.

Project description:Guided by the extensive knowledge of CRISPR-Cas9 molecular mechanisms, protein engineering can be an effective method in improving CRISPR-Cas9 toward desired traits different from those of their natural forms. Here, we describe a directed protein evolution method that enables selection of catalytically enhanced CRISPR-Cas9 variants (CECas9) by targeting a shortened protospacer within a toxic gene. We demonstrate the effectiveness of this method with a previously characterized Type II-C Cas9 from Acidothermus cellulolyticus (AceCas9) and show by enzyme kinetics an up to fourfold improvement of the in vitro catalytic efficiency by AceCECas9. We further evolved the more widely used Streptococcus pyogenes Cas9 (SpyCas9) and demonstrated a noticeable improvement in the SpyCECas9-facilitated homology directed repair-based gene insertion in human colon cancer cells.

| S-EPMC8182482 | biostudies-literature

Directed evolution of Escherichia coli with lower-than-natural plasmid mutation rates.

Project description:Unwanted evolution of designed DNA sequences limits metabolic and genome engineering efforts. Engineered functions that are burdensome to host cells and slow their replication are rapidly inactivated by mutations, and unplanned mutations with unpredictable effects often accumulate alongside designed changes in large-scale genome editing projects. We developed a directed evolution strategy, Periodic Reselection for Evolutionarily Reliable Variants (PResERV), to discover mutations that prolong the function of a burdensome DNA sequence in an engineered organism. Here, we used PResERV to isolate Escherichia coli cells that replicate ColE1-type plasmids with higher fidelity. We found mutations in DNA polymerase I and in RNase E that reduce plasmid mutation rates by 6- to 30-fold. The PResERV method implicitly selects to maintain the growth rate of host cells, and high plasmid copy numbers and gene expression levels are maintained in some of the evolved E. coli strains, indicating that it is possible to improve the genetic stability of cellular chassis without encountering trade-offs in other desirable performance characteristics. Utilizing these new antimutator E. coli and applying PResERV to other organisms in the future promises to prevent evolutionary failures and unpredictability to provide a more stable genetic foundation for synthetic biology.

| S-EPMC6158703 | biostudies-literature

Increasing protein production by directed vector backbone evolution.

Project description:Recombinant protein production in prokaryotic and eukaryotic organisms was a key enabling technology for the rapid development of industrial and molecular biotechnology. However, despite all progress the improvement of protein production is an ongoing challenge and of high importance for cost-effective enzyme production. With the epMEGAWHOP mutagenesis protocol for vector backbone optimization we report a novel directed evolution based approach to increase protein production levels by randomly introducing mutations in the vector backbone. In the current study we validate the epMEGAWHOP mutagenesis protocol for three different expression systems. The latter demonstrated the general applicability of the epMEGAWHOP method. Cellulase and lipase production was doubled in one round of directed evolution by random mutagenesis of pET28a(+) and pET22b(+) vector backbones. Protease production using the vector pHY300PLK was increased ~4-times with an average of ~1.25 mutations per kb vector backbone. The epMEGAWHOP does not require any rational understanding of the expression machinery and can generally be applied to enzymes, expression vectors and related hosts. epMEGAWHOP is therefore from our point of view a robust, rapid and straight forward alternative for increasing protein production in general and for biotechnological applications.

| S-EPMC3750827 | biostudies-literature

Machine learning-assisted directed protein evolution with combinatorial libraries.

Project description:To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

| S-EPMC6500146 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data